Novel Non-Invasive Approach for Predicting Gastric Cancer

Researchers have developed a novel method for detecting gastric cancer using plasma protein concentrations, offering a non-invasive and accurate approach for early detection. The study involved analyzing plasma samples from 138 participants, including patients with different gastric pathologies and healthy controls, to identify potential biomarkers. The results highlighted the promise of Leptin as a biomarker for detecting gastric preneoplasia, particularly in women, and nine other proteins showed differential plasma levels across patient groups. The use of multi-biomarker prediction models incorporating factors such as age, gender, and H. pylori status outperformed single-protein models, achieving high predictive accuracy.

Key Takeaways:

• The study analyzed plasma samples from 138 participants, including 39 patients with different gastric pathologies and 99 healthy controls.
• Mass spectrometry-based proteomics identified 691 proteins in plasma samples, with 213 candidate biomarkers capable of distinguishing cancer patients from healthy controls.
• Sparse PLS-DA identified 85 proteins that distinguish all patient groups, and subsequent ELISA validation confirmed Leptin as a promising biomarker for detecting gastric preneoplasia.
• The use of multi-biomarker prediction models incorporating factors such as age, gender, and H. pylori status achieved high predictive accuracy, with mean AUROC values of 85.3% for classifications cancer vs. non-cancer and 83.9% for cancer/preneoplasia vs. healthy/gastritis.
• The study demonstrated the potential for a non-invasive approach to detecting gastric cancer using plasma protein concentrations, offering a significant improvement in early detection and clinical management.

Statistics:

• 691 proteins were identified in plasma samples using mass spectrometry-based proteomics.
• 213 candidate biomarkers were capable of distinguishing cancer patients from healthy controls.
• 85 proteins were identified as factors that distinguish all patient groups using sparse PLS-DA.
• AUROC values of 85.3% and 83.9% were achieved for classifications cancer vs. non-cancer and cancer/preneoplasia vs. healthy/gastritis, respectively.
• 4-fold cross-validation repeated 10 times was used to validate the predictive models, achieving high accuracy.
• The top-performing models achieved AUROC values exceeding 94% for classifications cancer vs. non-cancer and cancer/preneoplasia vs. healthy/gastritis using the full cohort.

Sources:

• biorxiv.org/content/10.1101/2025.04.29.651246v1